Source code download:     [ .Rmd ]     [ .R ]

0.1 Introduction

This report describes the analysis of the RNA-Seq data set from Howard et al (2013). The corresponding FASTQ files were downloaded from GEO (Accession: SRP010938). This data set contains 18 paired-end (PE) read sets from Arabidposis thaliana. The details about all download steps are provided here.

Users want to provide here additional background information about the design of their RNA-Seq project.

0.1.1 Experimental design

Typically, users want to specify here all information relevant for the analysis of their NGS study. This includes detailed descriptions of FASTQ files, experimental design, reference genome, gene annotations, etc.

0.1.2 Workflow environment

NOTE: this section describes how to set up the proper environment (directory structure) for running systemPipeR workflows. After mastering this task the workflow run instructions can be deleted since they are not expected to be included in a final HTML/PDF report of a workflow.

  1. If a remote system or cluster is used, then users need to log in to the remote system first. The following applies to an HPC cluster (e.g. HPCC cluster).

    A terminal application needs to be used to log in to a user’s cluster account. Next, one can open an interactive session on a computer node with srun. More details about argument settings for srun are available in this HPCC manual or the HPCC section of this website here. Next, load the R version required for running the workflow with module load. Sometimes it may be necessary to first unload an active software version before loading another version, e.g. module unload R.

srun --x11 --partition=gen242 --mem=20gb --cpus-per-task 8 --ntasks 1 --time 20:00:00 --pty bash -l
# module unload R; module load load R/4.1.2
  1. Load a workflow template with the genWorkenvir function. This can be done from the command-line or from within R. However, only one of the two options needs to be used.

From command-line

$ Rscript -e "systemPipeRdata::genWorkenvir(workflow='rnaseq')"
$ cd rnaseq

From R

library(systemPipeRdata)
genWorkenvir(workflow = "rnaseq")
setwd("rnaseq")

  1. Optional: if the user wishes to use another Rmd file than the template instance provided by the genWorkenvir function, then it can be copied or downloaded into the root directory of the workflow environment (e.g. with cp or wget).

  2. Now one can open from the root directory of the workflow the corresponding R Markdown script (e.g. systemPipeChIPseq.Rmd) using an R IDE, such as nvim-r, ESS or RStudio. Subsequently, the workflow can be run as outlined below. For learning purposes it is recommended to run workflows for the first time interactively. Once all workflow steps are understood and possibly modified to custom needs, one can run the workflow from start to finish with a single command using runWF().

0.1.3 Load packages

The systemPipeR package needs to be loaded to perform the analysis steps shown in this report (H Backman and Girke 2016). The package allows users to run the entire analysis workflow interactively or with a single command while also generating the corresponding analysis report. For details see systemPipeR's main vignette.

library(systemPipeR)

To apply workflows to custom data, the user needs to modify the targets file and if necessary update the corresponding parameter (.cwl and .yml) files. A collection of pre-generated .cwl and .yml files are provided in the param/cwl subdirectory of each workflow template. They are also viewable in the GitHub repository of systemPipeRdata (see here). For more information of the structure of the targets file, please consult the documentation here. More details about the new parameter files from systemPipeR can be found here.

0.1.4 Import custom functions

Custom functions for the challenge projects can be imported with the source command from a local R script (here challengeProject_Fct.R). Skip this step if such a script is not available. Alternatively, these functions can be loaded from a custom R package.

source("challengeProject_Fct.R")

0.1.5 Experiment definition provided by targets file

The targets file defines all FASTQ files and sample comparisons of the analysis workflow. If needed the tab separated (TSV) version of this file can be downloaded from here and the corresponding Google Sheet is here.

targetspath <- "targetsPE.txt"
targets <- read.delim(targetspath, comment.char = "#")
DT::datatable(targets, options = list(scrollX = TRUE, autoWidth = TRUE))

0.2 Workflow steps

This tutorial will demonstrate how to build the workflow in an interactive mode, appending each step. The workflow is constructed by connecting each step via appendStep method. Each SYSargsList instance contains instructions for processing a set of input files with a specific command-line or R software and the paths to the corresponding outfiles generated by a particular command-line software/step.

To create a workflow within systemPipeR, we can start by defining an empty container and checking the directory structure:

library(systemPipeR)
sal <- SPRproject()
## Creating directory:  /home/dcassol/z_tests/GEN242_toPush/content/en/tutorials/sprnaseq/data 
## Creating directory '/home/dcassol/z_tests/GEN242_toPush/content/en/tutorials/sprnaseq/.SPRproject'
## Creating file '/home/dcassol/z_tests/GEN242_toPush/content/en/tutorials/sprnaseq/.SPRproject/SYSargsList.yml'
sal
## Instance of 'SYSargsList': 
##  No workflow steps added

0.2.1 Required packages and resources

The systemPipeR package needs to be loaded (H Backman and Girke 2016).

appendStep(sal) <- LineWise(code = {
    library(systemPipeR)
}, step_name = "load_SPR")

0.2.2 Read preprocessing

0.2.2.1 Read quality filtering and trimming

The function preprocessReads allows to apply predefined or custom read preprocessing functions to all FASTQ files referenced in a SYSargsList container, such as quality filtering or adapter trimming routines. The paths to the resulting output FASTQ files are stored in the outfiles slot of the SYSargsList object. The following example performs adapter trimming with the trimLRPatterns function from the Biostrings package. After the trimming step a new targets file is generated (here targets_trim.txt) containing the paths to the trimmed FASTQ files. The new targets file can be used for the next workflow step with an updated SYSargs2 instance, e.g. running the NGS alignments using the trimmed FASTQ files.

Here, we are appending this step to the SYSargsList object created previously. All the parameters are defined on the preprocessReads/preprocessReads-pe.yml and preprocessReads/preprocessReads-pe.cwl files.

appendStep(sal) <- SYSargsList(step_name = "preprocessing", targets = "targetsPE.txt",
    dir = TRUE, wf_file = "preprocessReads/preprocessReads-pe.cwl",
    input_file = "preprocessReads/preprocessReads-pe.yml", dir_path = "param/cwl",
    inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_",
        SampleName = "_SampleName_"), dependency = c("load_SPR"))

After, we can check the trimLRPatterns function in input parameter:

yamlinput(sal, "preprocessing")$Fct
## [1] "'trimLRPatterns(Rpattern=\"GCCCGGGTAA\", subject=fq)'"

After the preprocessing step, the outfiles files can be used to generate the new targets files containing the paths to the trimmed FASTQ files. The new targets information can be used for the next workflow step instance, e.g. running the NGS alignments with the trimmed FASTQ files. The appendStep function is automatically handling this connectivity between steps. Please check the Alignments step for more details.

0.2.2.2 FASTQ quality report

The following seeFastq and seeFastqPlot functions generate and plot a series of useful quality statistics for a set of FASTQ files including per cycle quality box plots, base proportions, base-level quality trends, relative k-mer diversity, length and occurrence distribution of reads, number of reads above quality cutoffs and mean quality distribution. The results are written to a PDF file named fastqReport.pdf.

appendStep(sal) <- LineWise(code = {
    fastq <- getColumn(sal, step = "preprocessing", "targetsWF",
        column = 1)
    fqlist <- seeFastq(fastq = fastq, batchsize = 10000, klength = 8)
    pdf("./results/fastqReport.pdf", height = 18, width = 4 *
        length(fqlist))
    seeFastqPlot(fqlist)
    dev.off()
}, step_name = "fastq_report", dependency = "preprocessing")
Figure 1: FASTQ quality report for 18 samples


0.2.3 Alignments

0.2.3.1 Read mapping with HISAT2

The following steps will demonstrate how to use the short read aligner Hisat2 (Kim, Langmead, and Salzberg 2015) in both interactive job submissions and batch submissions to queuing systems of clusters using the systemPipeR's new CWL command-line interface.

The following steps will demonstrate how to use the short read aligner Hisat2 (Kim, Langmead, and Salzberg 2015). First, the Hisat2 index needs to be created.

appendStep(sal) <- SYSargsList(step_name = "hisat2_index", dir = FALSE,
    targets = NULL, wf_file = "hisat2/hisat2-index.cwl", input_file = "hisat2/hisat2-index.yml",
    dir_path = "param/cwl", dependency = "load_SPR")

The parameter settings of the aligner are defined in the workflow_hisat2-pe.cwl and workflow_hisat2-pe.yml files. The following shows how to construct the corresponding SYSargsList object. Please note that the targets used in this step are the outfiles from preprocessing step.

appendStep(sal) <- SYSargsList(step_name = "hisat2_mapping",
    dir = TRUE, targets = "preprocessing", wf_file = "workflow-hisat2/workflow_hisat2-pe.cwl",
    input_file = "workflow-hisat2/workflow_hisat2-pe.yml", dir_path = "param/cwl",
    inputvars = c(preprocessReads_1 = "_FASTQ_PATH1_", preprocessReads_2 = "_FASTQ_PATH2_",
        SampleName = "_SampleName_"), rm_targets_col = c("FileName1",
        "FileName2"), dependency = c("preprocessing", "hisat2_index"))

To double-check the command line for each sample, please use the following:

cmdlist(sal, step = "hisat2_mapping", targets = 1)
## $hisat2_mapping
## $hisat2_mapping$M1A
## $hisat2_mapping$M1A$hisat2
## [1] "hisat2 -S ./results/M1A.sam  -x ./data/tair10.fasta  -k 1  --min-intronlen 30  --max-intronlen 3000  -1 ./results/M1A_1.fastq_trim.gz -2 ./results/M1A_2.fastq_trim.gz --threads 4"
## 
## $hisat2_mapping$M1A$`samtools-view`
## [1] "samtools view -bS -o ./results/M1A.bam  ./results/M1A.sam "
## 
## $hisat2_mapping$M1A$`samtools-sort`
## [1] "samtools sort -o ./results/M1A.sorted.bam  ./results/M1A.bam  -@ 4"
## 
## $hisat2_mapping$M1A$`samtools-index`
## [1] "samtools index -b results/M1A.sorted.bam  results/M1A.sorted.bam.bai  ./results/M1A.sorted.bam "

0.2.3.2 Read and alignment stats

The following provides an overview of the number of reads in each sample and how many of them aligned to the reference.

appendStep(sal) <- LineWise(code = {
    fqpaths <- getColumn(sal, step = "preprocessing", "targetsWF",
        column = "FileName1")
    bampaths <- getColumn(sal, step = "hisat2_mapping", "outfiles",
        column = "samtools_sort_bam")
    read_statsDF <- alignStats(args = bampaths, fqpaths = fqpaths,
        pairEnd = TRUE)
    write.table(read_statsDF, "results/alignStats.xls", row.names = FALSE,
        quote = FALSE, sep = "\t")
}, step_name = "align_stats", dependency = "hisat2_mapping")

The following shows the alignment statistics for a sample file provided by the systemPipeR package.

read.table("results/alignStats.xls", header = TRUE)[1:4, ]
##   FileName Nreads2x Nalign Perc_Aligned Nalign_Primary
## 1      M1A   115994 109977     94.81266         109977
## 2      M1B   134480 112464     83.62879         112464
## 3      A1A   127976 122427     95.66403         122427
## 4      A1B   122486 101369     82.75966         101369
##   Perc_Aligned_Primary
## 1             94.81266
## 2             83.62879
## 3             95.66403
## 4             82.75966

0.2.4 Read quantification

Reads overlapping with annotation ranges of interest are counted for each sample using the summarizeOverlaps function (Lawrence et al. 2013). The read counting is preformed for exonic gene regions in a non-strand-specific manner while ignoring overlaps among different genes. Subsequently, the expression count values are normalized by reads per kp per million mapped reads (RPKM). The raw read count table (countDFeByg.xls) and the corresponding RPKM table (rpkmDFeByg.xls) are written to separate files in the directory of this project. Parallelization is achieved with the BiocParallel package, here using 4 CPU cores.

0.2.4.1 Create a database for gene annotation

appendStep(sal) <- LineWise(code = {
    library(GenomicFeatures)
    txdb <- suppressWarnings(makeTxDbFromGFF(file = "data/tair10.gff",
        format = "gff", dataSource = "TAIR", organism = "Arabidopsis thaliana"))
    saveDb(txdb, file = "./data/tair10.sqlite")
}, step_name = "create_db", dependency = "hisat2_mapping")

0.2.4.2 Read counting with summarizeOverlaps in parallel mode using multiple cores

appendStep(sal) <- LineWise(code = {
    library(GenomicFeatures)
    library(BiocParallel)
    txdb <- loadDb("./data/tair10.sqlite")
    outpaths <- getColumn(sal, step = "hisat2_mapping", "outfiles",
        column = "samtools_sort_bam")
    eByg <- exonsBy(txdb, by = c("gene"))
    bfl <- BamFileList(outpaths, yieldSize = 50000, index = character())
    multicoreParam <- MulticoreParam(workers = 4)
    register(multicoreParam)
    registered()
    counteByg <- bplapply(bfl, function(x) summarizeOverlaps(eByg,
        x, mode = "Union", ignore.strand = TRUE, inter.feature = FALSE,
        singleEnd = FALSE, BPPARAM = multicoreParam))
    countDFeByg <- sapply(seq(along = counteByg), function(x) assays(counteByg[[x]])$counts)
    rownames(countDFeByg) <- names(rowRanges(counteByg[[1]]))
    colnames(countDFeByg) <- names(bfl)
    rpkmDFeByg <- apply(countDFeByg, 2, function(x) returnRPKM(counts = x,
        ranges = eByg))
    write.table(countDFeByg, "results/countDFeByg.xls", col.names = NA,
        quote = FALSE, sep = "\t")
    write.table(rpkmDFeByg, "results/rpkmDFeByg.xls", col.names = NA,
        quote = FALSE, sep = "\t")
    ## Creating a SummarizedExperiment object
    colData <- data.frame(row.names = SampleName(sal, "hisat2_mapping"),
        condition = getColumn(sal, "hisat2_mapping", position = "targetsWF",
            column = "Factor"))
    colData$condition <- factor(colData$condition)
    countDF_se <- SummarizedExperiment::SummarizedExperiment(assays = countDFeByg,
        colData = colData)
    ## Add results as SummarizedExperiment to the workflow
    ## object
    SE(sal, "read_counting") <- countDF_se
}, step_name = "read_counting", dependency = "create_db")

When providing a BamFileList as in the example above, summarizeOverlaps methods use by default bplapply and use the register interface from BiocParallel package. If the number of workers is not set, MulticoreParam will use the number of cores returned by parallel::detectCores(). For more information, please check help("summarizeOverlaps") documentation.

Shows count table generated in previous step (countDFeByg.xls). To avoid slowdowns of the load time of this page, ony 200 rows of the source table are imported into the below datatable view .

countDF <- read.delim("results/countDFeByg.xls", row.names = 1,
    check.names = FALSE)[1:200, ]
DT::datatable(countDF, options = list(scrollX = TRUE, autoWidth = TRUE))

A data slice of RPKM table (rpkmDFeByg.xls) is shown here.

read.delim("results/rpkmDFeByg.xls", row.names = 1, check.names = FALSE)[1:4,
    1:4]
##                M1A      M1B      A1A      A1B
## AT1G01010 5179.326 5955.322 7558.353 4856.097
## AT1G01020 1792.088 2964.078 2746.372 3344.252
## AT1G01030 1925.599 2212.258 3072.697 1402.614
## AT1G01040 4452.871 4494.494 5772.681 4540.367

Note, for most statistical differential expression or abundance analysis methods, such as edgeR or DESeq2, the raw count values should be used as input. The usage of RPKM values should be restricted to specialty applications required by some users, e.g. manually comparing the expression levels among different genes or features.

0.2.4.3 Sample-wise correlation analysis

The following computes the sample-wise Spearman correlation coefficients from the rlog transformed expression values generated with the DESeq2 package. After transformation to a distance matrix, hierarchical clustering is performed with the hclust function and the result is plotted as a dendrogram (also see file sample_tree.pdf).

appendStep(sal) <- LineWise(code = {
    library(DESeq2, quietly = TRUE)
    library(ape, warn.conflicts = FALSE)
    ## Extracting SummarizedExperiment object
    se <- SE(sal, "read_counting")
    dds <- DESeqDataSet(se, design = ~condition)
    d <- cor(assay(rlog(dds)), method = "spearman")
    hc <- hclust(dist(1 - d))
    pdf("results/sample_tree.pdf")
    plot.phylo(as.phylo(hc), type = "p", edge.col = "blue", edge.width = 2,
        show.node.label = TRUE, no.margin = TRUE)
    dev.off()
}, step_name = "sample_tree", dependency = "read_counting")
Figure 2: Correlation dendrogram of samples


0.2.5 Analysis of DEGs

The analysis of differentially expressed genes (DEGs) is performed with the glm method of the edgeR package (Robinson, McCarthy, and Smyth 2010). The sample comparisons used by this analysis are defined in the header lines of the targets.txt file starting with <CMP>.

0.2.5.1 Run edgeR

appendStep(sal) <- LineWise(code = {
    library(edgeR)
    countDF <- read.delim("results/countDFeByg.xls", row.names = 1,
        check.names = FALSE)
    cmp <- readComp(stepsWF(sal)[["hisat2_mapping"]], format = "matrix",
        delim = "-")
    edgeDF <- run_edgeR(countDF = countDF, targets = targetsWF(sal)[["hisat2_mapping"]],
        cmp = cmp[[1]], independent = FALSE, mdsplot = "")
}, step_name = "run_edger", dependency = "read_counting")

0.2.5.2 Add gene descriptions

appendStep(sal) <- LineWise(code = {
    library("biomaRt")
    m <- useMart("plants_mart", dataset = "athaliana_eg_gene",
        host = "https://plants.ensembl.org")
    desc <- getBM(attributes = c("tair_locus", "description"),
        mart = m)
    desc <- desc[!duplicated(desc[, 1]), ]
    descv <- as.character(desc[, 2])
    names(descv) <- as.character(desc[, 1])
    edgeDF <- data.frame(edgeDF, Desc = descv[rownames(edgeDF)],
        check.names = FALSE)
    write.table(edgeDF, "./results/edgeRglm_allcomp.xls", quote = FALSE,
        sep = "\t", col.names = NA)
}, step_name = "custom_annot", dependency = "run_edger")

0.2.6 Plot DEG results

Filter and plot DEG results for up and down regulated genes. The definition of up and down is given in the corresponding help file. To open it, type ?filterDEGs in the R console.

appendStep(sal) <- LineWise(code = {
    edgeDF <- read.delim("results/edgeRglm_allcomp.xls", row.names = 1,
        check.names = FALSE)
    pdf("results/DEGcounts.pdf")
    DEG_list <- filterDEGs(degDF = edgeDF, filter = c(Fold = 2,
        FDR = 20))
    dev.off()
    write.table(DEG_list$Summary, "./results/DEGcounts.xls",
        quote = FALSE, sep = "\t", row.names = FALSE)
}, step_name = "filter_degs", dependency = "custom_annot")
Figure 3: Up and down regulated DEGs with FDR of 1%


0.2.7 Venn diagrams of DEG sets

The overLapper function can compute Venn intersects for large numbers of sample sets (up to 20 or more) and plots 2-5 way Venn diagrams. A useful feature is the possibility to combine the counts from several Venn comparisons with the same number of sample sets in a single Venn diagram (here for 4 up and down DEG sets).

appendStep(sal) <- LineWise(code = {
    vennsetup <- overLapper(DEG_list$Up[6:9], type = "vennsets")
    vennsetdown <- overLapper(DEG_list$Down[6:9], type = "vennsets")
    pdf("results/vennplot.pdf")
    vennPlot(list(vennsetup, vennsetdown), mymain = "", mysub = "",
        colmode = 2, ccol = c("blue", "red"))
    dev.off()
}, step_name = "venn_diagram", dependency = "filter_degs")
Figure 4: Venn Diagram for 4 Up and Down DEG Sets


0.2.8 GO term enrichment analysis

0.2.8.1 Obtain gene-to-GO mappings

The following shows how to obtain gene-to-GO mappings from biomaRt (here for A. thaliana) and how to organize them for the downstream GO term enrichment analysis. Alternatively, the gene-to-GO mappings can be obtained for many organisms from Bioconductor’s *.db genome annotation packages or GO annotation files provided by various genome databases. For each annotation this relatively slow preprocessing step needs to be performed only once. Subsequently, the preprocessed data can be loaded with the load function as shown in the next subsection.

appendStep(sal) <- LineWise(code = {
    library("biomaRt")
    # listMarts() # To choose BioMart database
    # listMarts(host='plants.ensembl.org')
    m <- useMart("plants_mart", host = "https://plants.ensembl.org")
    m <- useMart("plants_mart", dataset = "athaliana_eg_gene",
        host = "https://plants.ensembl.org")
    go <- getBM(attributes = c("go_id", "tair_locus", "namespace_1003"),
        mart = m)
    go <- go[go[, 3] != "", ]
    go[, 3] <- as.character(go[, 3])
    go[go[, 3] == "molecular_function", 3] <- "F"
    go[go[, 3] == "biological_process", 3] <- "P"
    go[go[, 3] == "cellular_component", 3] <- "C"
    go[1:4, ]
    if (!dir.exists("./data/GO"))
        dir.create("./data/GO")
    write.table(go, "data/GO/GOannotationsBiomart_mod.txt", quote = FALSE,
        row.names = FALSE, col.names = FALSE, sep = "\t")
    catdb <- makeCATdb(myfile = "data/GO/GOannotationsBiomart_mod.txt",
        lib = NULL, org = "", colno = c(1, 2, 3), idconv = NULL)
    save(catdb, file = "data/GO/catdb.RData")
}, step_name = "get_go_annot", dependency = "filter_degs")

0.2.8.2 Batch GO term enrichment analysis

Apply the enrichment analysis to the DEG sets obtained the above differential expression analysis. Note, in the following example the FDR filter is set here to an unreasonably high value, simply because of the small size of the toy data set used in this vignette. Batch enrichment analysis of many gene sets is performed with the function. When method=all, it returns all GO terms passing the p-value cutoff specified under the cutoff arguments. When method=slim, it returns only the GO terms specified under the myslimv argument. The given example shows how a GO slim vector for a specific organism can be obtained from BioMart.

appendStep(sal) <- LineWise(code = {
    library("biomaRt")
    load("data/GO/catdb.RData")
    DEG_list <- filterDEGs(degDF = edgeDF, filter = c(Fold = 2,
        FDR = 50), plot = FALSE)
    up_down <- DEG_list$UporDown
    names(up_down) <- paste(names(up_down), "_up_down", sep = "")
    up <- DEG_list$Up
    names(up) <- paste(names(up), "_up", sep = "")
    down <- DEG_list$Down
    names(down) <- paste(names(down), "_down", sep = "")
    DEGlist <- c(up_down, up, down)
    DEGlist <- DEGlist[sapply(DEGlist, length) > 0]
    BatchResult <- GOCluster_Report(catdb = catdb, setlist = DEGlist,
        method = "all", id_type = "gene", CLSZ = 2, cutoff = 0.9,
        gocats = c("MF", "BP", "CC"), recordSpecGO = NULL)
    m <- useMart("plants_mart", dataset = "athaliana_eg_gene",
        host = "https://plants.ensembl.org")
    goslimvec <- as.character(getBM(attributes = c("goslim_goa_accession"),
        mart = m)[, 1])
    BatchResultslim <- GOCluster_Report(catdb = catdb, setlist = DEGlist,
        method = "slim", id_type = "gene", myslimv = goslimvec,
        CLSZ = 10, cutoff = 0.01, gocats = c("MF", "BP", "CC"),
        recordSpecGO = NULL)
    write.table(BatchResultslim, "results/GOBatchSlim.xls", row.names = FALSE,
        quote = FALSE, sep = "\t")
}, step_name = "go_enrich", dependency = "get_go_annot")

Shows GO term enrichment results from previous step. The last gene identifier column (10) of this table has been excluded in this viewing instance to minimize the complexity of the result. To avoid slowdowns of the load time of this page, only 10 rows of the source table are shown below.

BatchResult <- read.delim("results/GOBatchAll.xls")[1:10, ]
knitr::kable(BatchResult[, -10])
CLID CLSZ GOID NodeSize SampleMatch Phyper Padj Term Ont
M1-A1_up_down 26 GO:0050291 4 1 0.0039621 0.0396207 sphingosine N-acyltransferase activity MF
M1-A1_up_down 26 GO:0004345 6 1 0.0059375 0.0593750 glucose-6-phosphate dehydrogenase activity MF
M1-A1_up_down 26 GO:0050664 11 1 0.0108597 0.1085975 oxidoreductase activity, acting on NAD(P)H, oxygen as acceptor MF
M1-A1_up_down 26 GO:0052593 11 1 0.0108597 0.1085975 tryptamine:oxygen oxidoreductase (deaminating) activity MF
M1-A1_up_down 26 GO:0052594 11 1 0.0108597 0.1085975 aminoacetone:oxygen oxidoreductase(deaminating) activity MF
M1-A1_up_down 26 GO:0052595 11 1 0.0108597 0.1085975 aliphatic-amine oxidase activity MF
M1-A1_up_down 26 GO:0052596 11 1 0.0108597 0.1085975 phenethylamine:oxygen oxidoreductase (deaminating) activity MF
M1-A1_up_down 26 GO:0052793 12 1 0.0118414 0.1184141 pectin acetylesterase activity MF
M1-A1_up_down 26 GO:0008131 15 1 0.0147808 0.1478083 primary amine oxidase activity MF
M1-A1_up_down 26 GO:0016018 16 1 0.0157588 0.1575878 cyclosporin A binding MF

0.2.8.3 Plot batch GO term results

The data.frame generated by GOCluster can be plotted with the goBarplot function. Because of the variable size of the sample sets, it may not always be desirable to show the results from different DEG sets in the same bar plot. Plotting single sample sets is achieved by subsetting the input data frame as shown in the first line of the following example.

appendStep(sal) <- LineWise(code = {
    gos <- BatchResultslim[grep("M6-V6_up_down", BatchResultslim$CLID),
        ]
    gos <- BatchResultslim
    png("results/GOslimbarplotMF.png")
    goBarplot(gos, gocat = "MF")
    dev.off()
    png("results/GOslimbarplotBP.png")
    goBarplot(gos, gocat = "BP")
    dev.off()
    png("results/GOslimbarplotCC.png")
    goBarplot(gos, gocat = "CC")
    dev.off()
}, step_name = "go_plot", dependency = "go_enrich")
Figure 5: GO Slim Barplot for MF Ontology


0.2.9 Clustering and heat maps

The following example performs hierarchical clustering on the rlog transformed expression matrix subsetted by the DEGs identified in the above differential expression analysis. It uses a Pearson correlation-based distance measure and complete linkage for cluster joining.

appendStep(sal) <- LineWise(code = {
    library(pheatmap)
    geneids <- unique(as.character(unlist(DEG_list[[1]])))
    y <- assay(rlog(dds))[geneids, ]
    pdf("results/heatmap1.pdf")
    pheatmap(y, scale = "row", clustering_distance_rows = "correlation",
        clustering_distance_cols = "correlation")
    dev.off()
}, step_name = "heatmap", dependency = "go_enrich")
Figure 6: Heat Map with Hierarchical Clustering Dendrograms of DEGs


0.2.10 Version Information

appendStep(sal) <- LineWise(code = {
    sessionInfo()
}, step_name = "sessionInfo", dependency = "heatmap")

0.3 Running workflow

0.3.1 Interactive job submissions in a single machine

For running the workflow, runWF function will execute all the steps store in the SYSargsList workflow container. The execution will be on a single machine without submitting to a queuing system of a computer cluster. Besides, runWF allows the user to create a dedicated results folder for each workflow. This includes all the output and log files for each step. When these options are used, the output location will be updated by default and can be assigned to the same object.

sal <- runWF(sal)

0.3.2 Parallelization on clusters

Alternatively, the computation can be greatly accelerated by processing many files in parallel using several compute nodes of a cluster, where a scheduling/queuing system is used for load balancing.

The resources list object provides the number of independent parallel cluster processes defined under the Njobs element in the list. The following example will run 18 processes in parallel using each 4 CPU cores. If the resources available on a cluster allow running all 18 processes at the same time, then the shown sample submission will utilize in a total of 72 CPU cores.

Note, runWF can be used with most queueing systems as it is based on utilities from the batchtools package, which supports the use of template files (*.tmpl) for defining the run parameters of different schedulers. To run the following code, one needs to have both a conffile (see .batchtools.conf.R samples here) and a template file (see *.tmpl samples here) for the queueing available on a system. The following example uses the sample conffile and template files for the Slurm scheduler provided by this package.

The resources can be appended when the step is generated, or it is possible to add these resources later, as the following example using the addResources function:

resources <- list(conffile=".batchtools.conf.R",
                  template="batchtools.slurm.tmpl", 
                  Njobs=18, 
                  walltime=120, ## minutes
                  ntasks=1,
                  ncpus=4, 
                  memory=1024, ## Mb
                  partition = "short"
                  )
sal <- addResources(sal, step = c("hisat2_mapping"), resources = resources)
sal <- runWF(sal)

0.3.3 Visualize workflow

systemPipeR workflows instances can be visualized with the plotWF function.

plotWF(sal, out_format = "html", out_path = "plotWF.html")

0.4 Checking workflow status

To check the summary of the workflow, we can use:

sal
statusWF(sal)

0.5 Technical report

systemPipeR compiles all the workflow execution logs in one central location, making it easier to check any standard output (stdout) or standard error (stderr) for any command-line tools used on the workflow or the R code stdout.

sal <- renderLogs(sal)

0.6 Scientific report

systemPipeR auto-generates scientific analysis reports in HTML format.

sal <- renderReport(sal)

0.7 Accessing the scientific report

systemPipeR auto-generated analysis reports in HTML format, compiling all the workflow steps containing all scientifically relevant results.

sal <- renderReport(sal)

0.8 Funding

This project is funded by NSF award ABI-1661152.

References

H Backman, Tyler W, and Thomas Girke. 2016. “systemPipeR: NGS workflow and report generation environment.” BMC Bioinformatics 17 (1): 388. https://doi.org/10.1186/s12859-016-1241-0.
Howard, Brian E, Qiwen Hu, Ahmet Can Babaoglu, Manan Chandra, Monica Borghi, Xiaoping Tan, Luyan He, et al. 2013. “High-Throughput RNA Sequencing of Pseudomonas-Infected Arabidopsis Reveals Hidden Transcriptome Complexity and Novel Splice Variants.” PLoS One 8 (10): e74183. https://doi.org/10.1371/journal.pone.0074183.
Kim, Daehwan, Ben Langmead, and Steven L Salzberg. 2015. “HISAT: A Fast Spliced Aligner with Low Memory Requirements.” Nat. Methods 12 (4): 357–60.
Lawrence, Michael, Wolfgang Huber, Hervé Pagès, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T Morgan, and Vincent J Carey. 2013. “Software for Computing and Annotating Genomic Ranges.” PLoS Comput. Biol. 9 (8): e1003118. https://doi.org/10.1371/journal.pcbi.1003118.
Robinson, M D, D J McCarthy, and G K Smyth. 2010. “edgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data.” Bioinformatics 26 (1): 139–40. https://doi.org/10.1093/bioinformatics/btp616.